Railway stations function as nodes in transport networks and places in an urban environment. They have accessibility and environmental impacts, which contribute to property value. The literature on the effects of railway stations on property value is mixed in its finding in respect to the impact magnitude and direction, ranging from a negative to an insignificant or a positive impact. This paper attempts to explain the variation in the findings by meta-analytical procedures. Generally the variations are attributed to the nature of data, particular spatial characteristics, temporal effects and methodology. Railway station proximity is addressed from two spatial considerations: a local station effect measuring the effect for properties with in 1/4 mile range and a global station effect measuring the effect of coming 250 m closer to the station. We find that the effect of railway stations on commercial property value mainly takes place at short distances. Commercial properties within 1/4 mile rang are 12.2% more expensive than residential properties. Where the price gap between the railway station zone and the rest is about 4.2% for the average residence, it is about 16.4% for the average commercial property. At longer distances the effect on residential property values dominate. We find that for every 250 m a residence is located closer to a station its price is 2.3% higher than commercial properties. Commuter railway stations have a consistently higher positive impact on the property value compared to light and heavy railway/Metro stations. The inclusion of other accessibility variables (such as highways) in the models reduces the level of reported railway station impact.

Railway stations function as nodes in transport networks and places in an urban environment. They have accessibility and environmental impacts, which contribute to property value. The literature on the effects of railway stations on property value is mixed in its finding in respect to the impact magnitude and direction, ranging from a negative to an insignificant or a positive impact. This paper attempts to explain the variation in the findings by meta-analytical procedures. Generally the variations are attributed to the nature of data, particular spatial characteristics, temporal effects and methodology. Railway station proximity is addressed from two spatial considerations: a local station effect measuring the effect for properties with in 1/4 mile range and a global station effect measuring the effect of coming 250 m closer to the station. We find that the effect of railway stations on commercial property value mainly takes place at short distances. Commercial properties within 1/4 mile rang are 12.2% more expensive than residential properties. Where the price gap between the railway station zone and the rest is about 4.2% for the average residence, it is about 16.4% for the average commercial property. At longer distances the effect on residential property values dominate. We find that for every 250 m a residence is located closer to a station its price is 2.3% higher than commercial properties. Commuter railway stations have a consistently higher positive impact on the property value compared to light and heavy railway/Metro stations. The inclusion of other accessibility variables (such as highways) in the models reduces the level of reported railway station impact.

A hedonic pricing model is estimated to analyse the impact of railways on house prices in terms of distance to railway station, frequency of railway services and distance to the railway line. Correcting for a wide range of other determinants of house prices we find that dwellings very close to a station are on average about 25% more expensive than dwellings at a distance of 15 kilometres or more. A doubling of frequency leads to an increase of house values of about 2.5%, ranging from 3.5% for houses close to the station to 1.3% for houses far away. Finally we find a negative effect of distance to railways, probably due to noise effects. Two railway station references were used in the analysis: the nearest and most frequently chosen station in the post code area. This distinction indicates that railway station accessibility is a more complex concept than one might think. It involves competition between railway stations.

Abstract:This study models the choices of Dutch railway users (aggregated at the 4 digit post code area) for access mode and departure railway stations. For each post code area a set of four access modes: car, public transport, bicycle and walking and a set three departure railway stations are identified. A nested logit model is estimated based on 1440 post code areas using a number of access and rail station features. The access features include distance to the departure station, car ownership level, public transport frequency and travel time by public transport to the departure stations. The station features used in the estimation include rail service quality index and supplementary facilities such as availability of parking space and bicycle standing place. Distance has a negative effect on the utility of departure stations. A steeper effect is observed on the choice of departure stations accessed by the non-motorized modes of walking and bicycle. Availability of parking places and bicycle standing areas have a positive effect on the choice of departure railway stations accessed by car and bicycle respectively. Public transport frequency has a positive whereas public transport travel time has a negative effect on the choice of departure stations accessed by public transport. The rail service quality index of a station has a significant and positive effect on the choice of departure stations accessed by all modes.

A hedonic pricing model is estimated based on sales data from three metropolitan areas in the Netherlands (Amsterdam, Rotterdam and Enschede) to analyse the effect of railway accessibility on house prices. Railway accessibility is measured by both the distance to a railway station and an index of quality of railway services provided at the station. Two railway station considerations were taken: the nearest railway station and the most frequently chosen railway station. Correcting for a wide range of other determinants, the model based on the most frequently chosen station outperforms the model based on the nearest railway station in estimating the effect of railway accessibility. The dissimilarity between the results of the two models increases with the increase in the urbanisation level of the metropolitan area. generally on accessibility, and particularly on railway accessibility. Accessibility as provided by rail in particular has received some attention in the literature. Railway accessibility is generally explained in relation to railway stations. In order to single out the effect of railway stations on property values, it is suggested that stations should be regarded as nodes in a transport network and places in an area (Bertolini and Spit, 1998). Using this